A random forest classifier is a machine learning algorithm that is based on the concept of an ensemble learning method known as random forests. It is a supervised learning algorithm used for classification tasks. Random forests are made up of multiple decision trees, where each tree is built on a randomly selected subset of the training data and features. Each decision tree in the random forest independently makes a prediction, and the final prediction is determined by aggregating the predictions from all the trees, typically using majority voting.
Published in Chapter:
Empowering Health With an Advanced Multi-Disease Prediction System and Medical Encyclopaedia: Apna Clinic
Souradeep Paul (Institute of Engineering and Management, University of Engineering and Management, Kolkata, India), Swagata Pal (Institute of Engineering and Management, University of Engineering and Management, Kolkata, India), and
Lidia Ghosh (Institute of Engineering and Management, University of Engineering and Management, Kolkata, India)
Copyright: © 2023
|Pages: 21
DOI: 10.4018/979-8-3693-0044-2.ch010
Abstract
Disease prediction is vital for effective treatment decisions in healthcare organizations. This work focuses on predicting multiple diseases using an improvised Machine Learning concept called 'Apna Clinic.' The system analyzes patient health records to forecast diseases like diabetes, breast cancer, heart disease, kidney disease, and liver disease using data normalization and weighted feature extraction. Comparison with existing models and comprehensive error analysis ensure accurate predictions. The behavior model is stored and deployed via the Flask API, enabling reliable functionality. Users access the system, submit disease parameters, and receive their health status. The analysis helps identify serious diseases, monitor patients, and provide timely warnings or suggestions for treatment. In acute cases, the system locates specialized doctors nearby. Additionally, a disease compendium with information on symptoms, prevention, and treatment is provided. The aim is to enhance treatment decisions, empower individuals, and facilitate proactive healthcare actions.